language, tags, license, base_model, datasets, library_name, pipeline_tag

language	tags	license	base_model	datasets	library_name	pipeline_tag
en kn hi ta te mr gu	lora sft indic qwen2.5	other	Qwen/Qwen2.5-1.5B	ai4bharat/indic-align	mlx	text-generation

qwen1.5B-india-finetuned

Overview

This repository contains Qwen2.5-1.5B fine-tuned with LoRA on small Indic instruction-following datasets.
The LoRA adapters were merged into the base weights, producing a standalone checkpoint that can be used directly with mlx_lm.

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License

• The base model Qwen/Qwen2.5-1.5B is released under the Qwen License.
• This fine-tuned checkpoint is subject to the same license. Please review the terms before use, especially for commercial scenarios.
• Marked here as license: other to follow Hugging Face conventions.

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Training Configuration

• Method: LoRA-SFT (attention + MLP)
• LoRA hyperparams: r=16, alpha=32, dropout=0.05
• Max sequence length: 1024
• Steps: 1500
• Batch size: 1
• Optimizer: AdamW (default in mlx_lm)
• Hardware: Apple Silicon (MacBook Pro M4)
• Framework: mlx_lm

The training configuration YAML used can be found at: configs/qwen2.5-3b_lora.yaml

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Data

• Subsets from ai4bharat/indic-align were used: Dolly_T and Anudesh.
• Converted into completion-style prompts (prompt/completion pairs).
• The focus is on Indic languages (Kannada, Hindi, Tamil, Telugu, Marathi, Gujarati) with some English instructions.
• Preprocessed into train.jsonl / valid.jsonl (not included here).

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Usage

Run with mlx_lm:

python -m mlx_lm generate \
  --model 5ivatej/qwen2.5-1.5B-india-finetuned \
  --max-tokens 200 \
  --prompt "Reply ONLY in Kannada written in English letters. Question: kannada dalli mathadoo?\n\nAnswer:"